1. Title, Journal and Authors
Title : Emerging investigators series: a critical review of decision support systems for water treatment: making the case for incorporating climate change and climate extremes
Journal : Water Research & Technology
Authors : William J. Rasemana, Joseph R. Kasprzyka*, Fernando L. Rosario-Ortiza, Jenna R. Stewarta and Ben Livneh a
a Civil, Environmental,and Architectural Engineering (CEAE) Department at the University of Colorado Boulder
Research on modeling water demand forecasting through soft computing technology has been ongoing. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. This paper is described by comparing the pros and cons of each type of model. There are many types of ANN models and are sometimes sensitive to climate. In particular, the accuracy of water demand prediction is improved when using the wavelet-bootstrap ANN model. The researchers also concluded that they outperform conventional ARIMAs. System dynamics is a more mathematical model, but its application is more limited than other soft computing methods. Recently, the hybrid model, rather than the classical model method, can improve the prediction accuracy.
3. Originality & Creativity
The authors tried to unify the words and definitions used in related fields.
4. Application to research
When creating a model, there are many trade-offs between various factors such as cost and environmental factors. This paper introduces various papers that deal with these issues and presents brief opinions.
Jeongwoo Moon / Ph.D. program
Environmental Systems Engineering Lab.
School of Environmental Science & Engineering
Gwangju Institute of Science and Technology
1 Oryong-dong Buk-gu Gwangju, 500-712, Korea
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